Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective

Abstract

Subsidence, the downward vertical land motion (VLM), plays a pivotal role in contributing to the risk of coastal flooding. Accurately estimating VLM and identifying its potential features related to subsidence can provide crucial information for stakeholders to make better-informed decisions. This study aimed to estimate large-scale subsidence at the Texas Gulf Coast and identify potential subsidence features using explainable models. Nine potential features were considered for modeling the VLM, ranging from natural terrain variations to anthropogenic activities. These features were used to train a random forest (RF) machine learning model. Explainable artificial intelligence (XAI) techniques including SHapley Additive exPlanations (SHAP) and impurity- and permutation-based feature importance were used to identify the contributions to subsidence. The results demonstrated favorable performance of the RF model, achieving an $R^{2}$ value of 0.56 during validation. XAI results underscored the significance of the digital elevation model in explaining subsidence at the Texas Coast. Additionally, XAI analysis highlighted the overall contribution of subsidence from anthropogenic activities, such as hydrocarbon extraction and groundwater withdrawal. Furthermore, the sample-level SHAP map provided detailed and reasonable subsidence-attribution results across the study area, showing potential for automatic and data-driven explanations of the VLM.

Keywords

• Attribution analysis
• coastal land subsidence
• explainable artificial intelligence (XAI)
• machine learning (ML)